Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Jurečková (2006)
Quantile RegressionJournal of the American Statistical Association, 101
C. Hafner (2003)
On forecasting Exchange Rate Volatility, 11
Marc Paolella (2001)
Testing the stable Paretian assumptionMathematical and Computer Modelling, 34
G. Bassett, R. Koenker (1978)
Asymptotic Theory of Least Absolute Error RegressionJournal of the American Statistical Association, 73
G. Ljung, G. Box (1978)
On a measure of lack of fit in time series modelsBiometrika, 65
T. Bollerslev, J. Wooldridge (1992)
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariancesEconometric Reviews, 11
Matthew Pritsker (1996)
Evaluating Value at Risk Methodologies: Accuracy versus Computational TimeJournal of Financial Services Research, 12
M. Loretan, P. Phillips (1994)
Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasetsJournal of Empirical Finance, 1
J. Corcoran (2002)
Modelling Extremal Events for Insurance and FinanceJournal of the American Statistical Association, 97
James Taylor (1999)
A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns, 7
Daniel Nelson, Dean Foster (1994)
Asypmtotic Filtering Theory for Univariate Arch Models
F. Palm (1996)
7 GARCH models of volatilityHandbook of Statistics, 14
G. Barone-Adesi, K. Giannopoulos, L. Vosper (2002)
Backtesting Derivative Portfolios with Filtered Historical Simulation (Fhs)Risk Management
A. McNeil, R. Frey (2000)
Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approachJournal of Empirical Finance, 7
(1996)
Overview of the Amendment to the Capital Accord to Incorporate Market Risks
John Knight, Stephen Satchell (2004)
Forecasting Volatility in Financial Markets : A Review
P. Giot, S. Laurent (2001)
Modelling Daily Value-at-Risk Using Realized Volatility and Arch Type ModelsCapital Markets: Asset Pricing & Valuation eJournal
Zhuanxin Ding, C. Granger, R. Engle (1993)
A long memory property of stock market returns and a new model
H. Lytton (1981)
An extended family.
M. Hinich (2005)
Time Series Analysis by State Space MethodsTechnometrics, 47
S. Koopman, Borus Jungbacker, Eugenie Uspensky (2004)
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility MeasurementsEconometrics eJournal
Vijay Bawa (1978)
Safety-First, Stochastic Dominance, and Optimal Portfolio ChoiceJournal of Financial and Quantitative Analysis, 13
(1996)
SUPERVISORY FRAMEWORK FOR THE USE OF "BACKTESTING" IN CONJUNCTION WITH THE INTERNAL MODELS APPROACH TO MARKET RISK CAPITAL REQUIREMENTS
Jeremy Berkowitz, James O'Brien (2001)
How accurate are Value-at-Risk models at commercial banks?Social Science Research Network, 2001
G. Barone-Adesi, K. Giannopoulos, L. Vosper (1999)
VaR without correlations for portfolios of derivative securitiesJournal of Futures Markets, 19
T. Lux (2006)
The Markov-Switching Multi-Fractal Model of Asset Returns: Estimation via GMM and Linear Forecasting of Volatility
John Galbraith, Turgut Kıṣınbay (2005)
Content horizons for conditional variance forecastsInternational Journal of Forecasting, 21
Philippe Jorion (1996)
Value at risk: the new benchmark for controlling market risk
M. Martens (2001)
Forecasting daily exchange rate volatility using intraday returnsJournal of International Money and Finance, 20
F. Palm (1996)
GARCH Models of Volatility, 14
Jón Dańıelsson, Yuji Morimoto (2000)
Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese MarketMonetary and and Economic Studies, 18
T. Bollerslev, R. Engle, J. Wooldridge (1988)
A Capital Asset Pricing Model with Time-Varying CovariancesJournal of Political Economy, 96
Jón Dańıelsson, C. Vries (2000)
Value-at-Risk and Extreme ReturnsAnnals of economics and statistics
R. Fisher, L. Tippett (1928)
Limiting forms of the frequency distribution of the largest or smallest member of a sampleMathematical Proceedings of the Cambridge Philosophical Society, 24
M. Pritsker (2001)
The Hidden Dangers of Historical SimulationJournal of Financial Abstracts eJournal
K. Dowd (2002)
Measuring Market Risk
E. Jondeau, M. Rockinger (2001)
Entropy Densities: With an Application to Autoregressive Conditional Skewness and KurtosisEconometrics: Econometric & Statistical Methods - General eJournal
Chris Brooks, A. Clare, J. Molle, G. Persand (2005)
A Comparison of Extreme Value Theory Approaches for Determining Value at RiskCapital Markets: Market Efficiency eJournal
P. Hansen, Asger Lunde (2004)
A Forecast Comparison of Volatility Models: Does Anything Beat a Garch(1,1)?Capital Markets: Market Microstructure eJournal
Reto Gallati (2003)
Risk Management and Capital Adequacy
Akhtar Siddique, Campbell Harvey (1999)
Autoregressive Conditional SkewnessJournal of Financial and Quantitative Analysis, 34
Markus Haas, S. Mittnik, Marc Paolella (2004)
Mixed Normal Conditional HeteroskedasticityJournal of Financial Econometrics, 2
(1995)
An Internal Model-Based Approach to Market Risk Capital Requirements
S. Mittnik, Marc Paolella (2000)
Conditional density and value‐at‐risk prediction of Asian currency exchange ratesJournal of Forecasting, 19
Jón Dańıelsson, C. Vries (1998)
Beyond the Sample: Extreme Quantile and Probability Estimation
Eric Ghysels, A. Harvey, É. Renault (1996)
5 Stochastic volatilityHandbook of Statistics, 14
Yong Bao, Tae-Hwy Lee, Burak Saltoǧlu (2006)
Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality CheckJournal of Forecasting, 25
J. Gonzalo, Jose Olmo (2004)
Which Extreme Values Are Really ExtremeJournal of Financial Econometrics, 2
Jeremy Berkowitz, James O'Brien (2001)
How Accurate are Value-at-Risk Models at Commercial BanksRisk Management & Analysis in Financial Institutions eJournal
Chris Brooks, G. Persand (2003)
Volatility forecasting for risk managementJournal of Forecasting, 22
V. Chernozhukov, Len Umantsev (2000)
Conditional value-at-risk: Aspects of modeling and estimationEmpirical Economics, 26
(2001)
Finance and Economics Discussion Series 27. Board of Governors of the Federal Reserve System
Yong Bao, Tae-Hwy Lee (2004)
A Test for Density Forecast Comparison with Applications to Risk Management
R. Adler, R. Feldman, C. Gallagher (1998)
Analysing stable time series
(1978)
Econometrica
Mark McComb (2000)
A Practical Guide to Heavy TailsTechnometrics, 42
Jose Lopez (1996)
Regulatory Evaluation of Value-at-Risk ModelsFederal Reserve Bank of New York Research Paper Series
R. Koenker, Quanshui Zhao (1996)
Conditional Quantile Estimation and Inference for Arch ModelsEconometric Theory, 12
S. Rachev (2003)
Handbook of heavy tailed distributions in finance
Philippe Artzner, F. Delbaen, J. Eber, D. Heath (1999)
Coherent Measures of RiskMathematical Finance, 9
(2004)
Regime Switching and the Estimation of Multifractal Processes.’
(1986)
Testing Statistical Hypotheses, 2nd ed
F. Diebold, Til Schuermann, J. Stroughair (1998)
Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk ManagementOrganizations & Markets eJournal
S. Mittnik, Marc Paolella, S. Rachev (1998)
A tail estimator for the index of the stable paretian distributionCommunications in Statistics-theory and Methods, 27
R. Koenker, G. Bassett (2007)
Regression Quantiles
J. Pickands (1975)
Statistical Inference Using Extreme Order StatisticsAnnals of Statistics, 3
C. Pedersen, S. Satchell (1998)
An Extended Family of Financial-Risk MeasuresThe Geneva Papers on Risk and Insurance Theory, 23
Peter Christoffersen (2003)
Elements of Financial Risk Management
Markus Haas, S. Mittnik, Marc Paolella (2004)
A New Approach to Markov-Switching GARCH ModelsJournal of Financial Econometrics, 2
B. Hill (1975)
A Simple General Approach to Inference About the Tail of a DistributionAnnals of Statistics, 3
T. Bollerslev (1986)
Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 31
T. Lux, T. Kaizoji (2004)
Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models
Richard Smith (1987)
Estimating tails of probability distributionsAnnals of Statistics, 15
S. Mittnik, Marc Paolella (2003)
Prediction of Financial Downside-Risk with Heavy-Tailed Conditional DistributionsCapital Markets: Asset Pricing & Valuation
I. Venetis, D. Peel (2005)
Non-linearity in stock index returns: the volatility and serial correlation relationshipEconomic Modelling, 22
Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study we compare the out-of-sample performance of existing methods and some new models for predicting value-at-risk (VaR) in a univariate context. Using more than 30 years of the daily return data on the NASDAQ Composite Index, we find that most approaches perform inadequately, although several models are acceptable under current regulatory assessment rules for model adequacy. A hybrid method, combining a heavy-tailed generalized autoregressive conditionally heteroskedastic (GARCH) filter with an extreme value theory-based approach, performs best overall, closely followed by a variant on a filtered historical simulation, and a new model based on heteroskedastic mixture distributions. Conditional autoregressive VaR (CAViaR) models perform inadequately, though an extension to a particular CAViaR model is shown to outperform the others.
Journal of Financial Econometrics – Oxford University Press
Published: Oct 12, 2006
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.